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Design of Experiments
Finding the Power Factors in Your Process
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February 2007
- Vol 4, Issue 1
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In This Issue
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Quick Links
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Design of Experiments is one the most powerful,
yet
least understood and used, of the improvement tools
available to manufacturing organizations. The
financial payback period achieved from using DOE,
especially screening experiments, is often measured
in months and weeks, not years. What other
investment in time and resources can generate that
level of return over and over again?
Some believe DOE is an advanced SPC technique. It
is not. In discussions regarding quality, we have a
tendency to use “control” and “improvement”
interchangeably, but the implied meanings of the two
terms are fundamentally different. By nature, control
mechanisms prevent change. But improvement is
change –specifically change for the better. The
statistical tools of control, SPC, are designed to
prevent change, not cause it. SPC helps us maintain
the status quo. The statistical tools of improvement
are the families of designed experiments. DOE is the
antithesis of control.
However, SPC and DOE are complementary. It is
best to ensure a process is stable before introducing
deliberate changes with a designed experiment.
Without process stability (SPC), the experimental
results may be confounded with special causes of
variation. DOE analysis techniques are based on the
ability to change variables systematically and then
determine if one output is statistically different from
another. If a process is unstable and full of special
causes of variation, we will have difficulty in
determining if differences in experimental outputs are
due to the changes introduced through the
experiment or due to the instability of the process.
Application of SPC gives us the solid foundation
needed to effectively use DOE.
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The major types of Designed Experiments are Full
Factorials, Fractional Factorials, Screening
Experiments, Response Surface Analysis (RSA), EVOP
and Mixture Experiments. Our DOE course focuses on
Screening Experiments. Screening experiments are
the ultimate fractional factorial experiments. They
literally screen the factors, or variables, in the
process and determine which are the critical
variables that affect the process output and from a
manufacturing standpoint, usually give the “biggest
bang for the buck.”
There are two major families of screening
experiments: Drs. Plackett and Burman developed
the original family of screening experiments matrices
in the 1940s. Dr. Taguchi adapted the Plackett–
Burman screening designs. He modified the Plackett–
Burman design approach so that the experimenter
could assume that interactions are not significant,
yet could test for some two-way interactions at the
same time.
Our
DOE: Screening Experiments
course consists of
three units: Background for DOE, Plackett-Burman
Experiments, and Taguchi Techniques. Each unit
contains lessons that divide the content into
manageable learning segments. At the end of each
unit, learners have access to a Challenge to test
their comprehension of the body of knowledge
covered in the unit.
Want to try out a free DOE training lesson? Click here>>>
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 A DOE effort will fail if not properly planned. The
team or individual responsible for the experiment
needs to take the time to think through the entire
activity. Without good planning, the designed
experiment might yield poor results or, even worse,
lead to misleading conclusions. Working through
these 8 considerations will help ensure a successful
experimental outcome.
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Design and
Communicate the Objective
The objective will
generally be one of three forms: The
“Biggest” (to maximize the response), the “Smallest”
(to minimize the response)
or the “Closest-to-Target” (to hit a target)
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Define the
Process
Define the
boundaries of the process to be experimented
upon. This could be just internal
processes or it could include the full
extended process in which the processes of suppliers
and/or customers are
studied along with internal processes.
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Select a
Response
and Measurement System
Responses are the
outputs, or the dependent variables, of
the process. In analyzing a designed experiment, you
can use as many responses
as you are willing to measure. A good
measurement system is one that is
accurate, repeatable, reproducible, stable, and
linear. Taking good samples is
a critical aspect of the measurement system.
The samples from each experimental
run must be representative of the response during
that run.
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Ensure that the
Measurement System is Adequate
Make sure the
measurement system has been calibrated. If
the measurement system is not repeatable and
reproducible, the results of the
designed experiment will not be valid. It is
prudent to conduct a GR&R before
investing in the time, effort and funds for conducting
a designed experiment.
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Select Factors
to be
Studied
Factors are the
independent variables that will affect the
response; select those factors that should have the
greatest impact on the
response. Ensure that it is practical, feasible,
and cost effective to select a
factor to be studied and to change its level.
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Select the
Experimental Design
The type of design is
highly dependent on the number of
factors to be studied. Screening experiments
are usually the best design choice
early in an experimental sequence when many factors
are to be explored.
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Set Factor
Levels
Be bold and set the
levels at the edges of the operating
window for the process when conducting screening
experiments.
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Final Design
Considerations
Final considerations
include: Selecting the experimental
matrix to use; deciding how to estimate the
experimental error and planning the
experiment so that any external sources of variation
are minimized.
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We hope that this issue of our newsletter has gotten
you thinking about how you might be able to use
design of experiments in your job and on problem-
solving teams. While the heavy duty statistics
behind DOEs might be a little daunting, there are lots
of software programs out there today to help you do
the job. What the software can't tell you is where
to use a DOE or how to set one up. We hope that
we have gotten you thinking about that with our
newsletter.
Next month our newsletter will focus on Measurement
Systems Analysis.
Stay warm,
Robin McDermott
Resource Engineering, Inc.
phone:
802-496-5888 or 800-810-8326
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Winter has finally arrived in Vermont. Right now it is
snowing and and today we are having a relative
heatwave with temperatures around 10°F. Last
night it was -15°F! After a brisk morning snowshoe
trek, I spent sometime today looking at seed
catalogs
and planning out my summer garden. That got me
thinking about Design of Experiments (DOE) of all
things! That's because DOE techniques were first
developed by Sir Ronald Fisher in his efforts to
increase agricultural yields in the early 1900s.
Thanks to Fisher and those who built on his work,
today we have techniques that can be used in
manufacturing and other fields to help determine the
key factors in processes. Knowing those key factors
will enable us to optimize the process for improved
quality and productivity.